A3C fails to solve MountainCar-v0 enviroment (implementation by OpenAi gym)
I don't know about your first question, but I got a  basic policy gradient approach with the kinetic energy as reward working on MountainCar-v0. 

You can implement it based on this blog and the notebook you find there. It uses an MLP with one hidden layer of size 128 and standard policy gradient learning. 

The reward engineering boils down to replacing the reward variable with the kinetic energy $v^2$ (no potential energy and no constant factor, the reward itself is not used). I takes $&gt;1000$ episodes to solve the environment consistently.

I'm afraid the solution is not very satisfactory and I don't have the feeling there is much to learn from it. The solution is originally for the cartpole problem and it stops working for me if I change hyperparameters/optimizer or the specifics of the reward.